A new rapid review finds that a number of interventions may reduce hospital outpatient no-shows
Outpatient no-shows have important implications for costs and the quality of care in the NHS. In 2019/2020, there were over five million outpatient no-shows across the NHS in the United Kingdom, with an estimated annual cost as high as £750 million.
Predictive models could be used to identify scheduled appointments that are at high risk of no-show. Healthcare staff could then intervene in a targeted manner, to reduce the risk that these appointments will be missed.
Researchers from the National Institute for Health and Care Research (NIHR) Applied Research Collaboration Greater Manchester (ARC-GM) and The University of Manchester have undertaken a review of interventions aiming to reduce outpatient no-shows by using predictive models. The researchers examined the effectiveness of these interventions, as well as the associated costs, acceptability to staff and patients, and effect on health inequities.
The review found that several promising interventions can be used in combination with predictive models. Specifically, predictive model-based reminders and predictive model-based patient navigator phone calls are probably effective at reducing no-shows. However, it is uncertain whether predictive model-based overbooking is effective.
Additionally, the researchers concluded that more evidence is needed regarding the cost-effectiveness, acceptability, and equity of all identified interventions.
Dr Theodora Oikonomidi, Research Associate at the University of Manchester and NIHR ARC-GM, who led the review,
“this review is timely, as an increasing number of health care organisations turn to using predictive models to manage outpatient appointments, in hopes that this technology will help to manage the appointment backlog created due to the COVID-19 pandemic.”
The full findings of this work have been published in Journal of the American Medical Informatics Association:
NIHR ARC-GM are also working in collaboration with Manchester University NHS Foundation Trust as they implement an integrated and innovative Electronic Patient Record (EPR) solution called Hive.
We are looking to describe how a predictive model within Hive that supports the identification of patients who are likely to not attend their outpatient appointment can be used to help staff manage outpatient no-shows in the trust. More information about this research can be found here.
published 11/01/2023